The invention relates to a novel application of a combinatorial materials development with minimum variance and maximum integration. In particular, the invention is a system and method of project development for a combinatorial materials development process using DFSS techniques.
As illustrated in FIG. 1 Combinatorial materials development (CMD) is an experimental approach to rapidly identify or optimize new material compositions or processes. CMD uses a parallel approach to generate thousands of target materials. The targets are evaluated quickly and reliably using automated analytical systems. The final step is to use statistical data analysis and visualization to identify promising leads.
FIG. 2 illustrates the transition from traditional chemical research to Combinatorial technology. From the 1890s to the 1990s chemists as individuals might perform one or two experiments per day with experimental sizes limited to 1 to 1000 grams per experiment. These 100 to 500 experiments per year might lead to 1 or 2 new leads per year.
Combinatorial technology 10 typically comprises several steps including: Experimental Planning 12; Sample Preparation 14; Chemical Reaction 16; Analytical Preparation 18; Chemical Analysis 20; and Data Analysis 22. These steps will be discussed further in relation to the use of six sigma techniques. In the 1990s, development of Combinatorial Technology permitted a team approach using experimental sizes 1 to 100 milligrams per experiment with 10 to 200 or more experiments per day. Depending on the chemistry involved, the 1000 to 10,000 or more experiments per year are likely to generate 10 or more new leads per year. The Combinatorial Technology approach can be used for discoveries of new materials when there are many possible components and small changes in components cause big changes in material properties. The CMD process may not be as effective for minimizing material problems where components are few and well known. The combinatorial approach was developed to overcome competitive threats, address the need for speed, reduced cost, and broad patent coverage, and to deal with increasing system complexity and expectations. The advantages of the CMD approach are high-speed innovation with the possibility of broad patent protection. The hardware and software that make CMD possible are now available.
For any process (business, manufacturing, service, research, etc.), the sigma value is a metric that indicates how well that process is performing. The higher the sigma value, the better the output. Sigma measures the capability of the process to perform defect-free-work, where a defect is synonymous with customer dissatisfaction. With six sigma, the common measurement index is defects-per-unit where a unit can be virtually anything. Examples include a component, a piece part of a jet engine, and an administrative procedure. The sigma value indicates how often defects are likely to occur. As sigma increases, customer satisfaction goes up along with improvement of other metrics (e.g., cost and cycle time).
The six sigma methodology has been used by a number of companies such as Motorola Semiconductors, Texas Instruments, Allied Signal and Digital Corporation. All of these companies use this process for a specific application such as semiconductor manufacturing in the case of Motorola and Texas Instruments. General Electric Company, the assignee of this application, has used six sigma technology in a wide number of areas.
FIG. 3 is a flowchart of a design for six sigma (DFSS) process in new product development. The overall DFSS process of FIG. 3 is divided into four sub-processes labeled Identify, Design, Optimize and Validate. Each sub-process includes sub-steps. The Identify sub-process includes sub-steps 102 and 104. The Design sub-process includes sub-steps 106-112. The Optimize sub-process includes sub-steps 114-126. The Validate sub-process includes sub-steps 128-134. The DFSS process shown in FIG. 3 is useful for improving the process of designing a product or procedure. The invention can also be applied to other six sigma processes such as the Measure, Analyze, Improve and Control (MAIC) process used for improving processes (such as manufacturing processes or business processes).
The six sigma process includes a method for identifying critical to quality (CTQ) dependencies in quality function deployment. Quality function deployment (QFD) is a methodology for documenting and breaking down customer requirements into manageable and actionable details. The concept of xe2x80x9chouses of qualityxe2x80x9d has been used to represent the decomposition of higher level requirements such as critical to quality characteristics or CTQ""s (also referred to as Y""s) into lower level characteristics such as key control parameters or KCP""s (also referred to as X""s). FIG. 4 depicts a conventional house of quality hierarchy in which high level requirements such as customer requirements are decomposed into lower level characteristics such as key manufacturing processes and key process variables within the manufacturing processes.
Each house of quality has previously corresponded to a stage or level of the process of designing a product. At the highest level, represented as house of quality #1152, customer requirements are associated with functional characteristics of a product. At the next level of the design process, represented as house of quality #2154, the functional characteristics of the new product are associated with new product characteristics. At the next level of the design process, represented as house of quality #3206, the part characteristics are associated with manufacturing processes. At the next level of the design process, represented as house of quality #4208, the manufacturing processes are associated with manufacturing process variables.
Conventionally, new chemical entities with useful properties are generated by identifying a chemical compound (called a xe2x80x9clead compoundxe2x80x9d) with some desirable property or activity, creating variants of the lead compound, and evaluating the property and activity of those variant compounds. Examples of chemical entities with useful properties include paints, finishes, plasticizers, surfactants, scents, flavorings, and bioactive compounds, but can also include chemical compounds with any other useful property that depends upon chemical structure, composition, or physical state. Chemical entities with desirable biological activities include drugs, herbicides, pesticides, veterinary products, and the like.
One deficiency in traditional chemical research pertains to the first step of the conventional approach, i.e., the identification of lead entities. As stated by Claudia M. Caruana, xe2x80x9cCombinatorial Chemistry Promises Better Catalysts and Materialsxe2x80x9d, Chem. Eng. Prog., October 1998, p 11-14, xe2x80x9cTypically, catalyst discovery involves inefficient trial-and-error, because catalytic activity is difficult to screen.xe2x80x9d Consequently, a fundamental limitation of the conventional approach is the ability to generate and analyze large numbers of catalyst candidates. The generation of such candidates is very labor intensive and time consuming. For example, it takes many chemist years to produce and evaluate even a small subset of the variants in a single catalyst system. Caruana, in the article referenced above, states that xe2x80x9cconventional discovery strategies usually are based on the time-consuming xe2x80x9cone-sample-at-a-timexe2x80x9d approach, which can take months or years to determine suitable candidate materials.xe2x80x9d
Recently, attention has been focused on the use of combinatorial chemical methods to assist in the generation of new materials development leads. xe2x80x9cCombinatorial chemistry uses a parallel approach to discover thousands of target materials and then produces xe2x80x9clibrariesxe2x80x9d of these substances quickly.xe2x80x9d (Ref.: Caruana) A combinatorial materials library is a collection of diverse materials generated by either chemical synthesis or by a combination of formulation and process steps, combining chemical xe2x80x9cbuilding blocksxe2x80x9d such as reagents. For example, a combinatorial catalyst library is formed by combining precursor solutions to generate an array of formulations, subjecting it to appropriate processing conditions to produce (possibly) active catalysts, and evaluating the activity of each formulation. Millions of potential catalysts or other materials theoretically can be generated through such combinatorial mixing of chemical building blocks and multiple process steps. For example, Peter Schultz in xe2x80x9cGenerating New Molecular Function: a Lesson from Naturexe2x80x9d, Angew. Chem. Int. Ed, 1999, 38, 36-54, has observed that xe2x80x9cGiven approximately 60 elements in the periodic table that can be used to make compositions consisting of three, four, five, or even six elements, the universe of possible new compounds remains largely uncharted.xe2x80x9d
However there is a need for a system and method for efficiently and effectively generating new leads designed for specific utilities. Combinatorial technology has the ability to develop many leads, but the variability in the results develops many inefficiencies. There is a need to reduce the variability of the results in combinatorial technology processes.
Acordingly, the invention relates to a system for implementing a combinatorial chemistry research project using Design for Six Sigma (DFSS) techniques. In an exemplary embodiment, a novel application of a combinatorial materials development with minimum variance and maximum integration is provided. In particular, the present invention provides a system and method of project development of a combinatorial materials development process, such as catalyst development, using DFSS techniques.
The process has four major elements. The first element is the use of a Design for Six Sigma (DFSS) process mapping to convert a complex and disorganized process structure to an organized structure that can be further analyzed.
The second element comprises the use of quality function deployment (QFD) houses as a method of flowing critical to quality characteristics (CTQ""s) through a research project. After the customer CTQ""s and the measures are developed conventionally in House 1, a novel usage of QFD is done by making the entire project the xe2x80x9cproductxe2x80x9d which is analyzed by House 2. Individual process elements of the project are analyzed for CTQ""s, and those CTQ""s become the xe2x80x9chow""sxe2x80x9d in the QFD House 2. Doing this allows effective prioritization of all the Measure, Analyze, Improve and Control (MAIC) projects applied to each process element.
The third element comprises a transfer function that connects the overall steps of the project to the output which is measured as variability not as mean. Score cards are used as the xe2x80x9cfunctionxe2x80x9d to total the variabilities of each process step.
The fourth and final element comprises an extension of design of experiment (DOE) techniques. Conventional DOE""s, discussed above, are ineffective for combinatorial chemistry because of the large size and complexity of the experiments. Additionally, in the context of these particular experiments for catalyst development, generally only synergistic effects of co-catalyst combinations are meaningful. This requires novel DOE approaches such as full combinatorial designs.
The combined use of combinatorial techniques and six sigma techniques as disclosed herein reduces the variabilities in the results, thereby producing a better result from the development program. These and other features and advantages of the present invention will be apparent from the following brief description of the drawings, detailed description, and appended claims.